December 16, 2019

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Content of the Presentation

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  • Introduction
  • Methodology
  • Simulation Results
  • Discussion

Introduction

Multiple imputation

  • Missing data is ubiquitous (Allison 2001).
  • Ad hoc solutions may yield invalid inferences (Van Buuren 2018).
  • Rubin (1987) proposed the framework of MI.

Multiple imputation



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Algorithmic convergence



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Methodology

Mock code

simulation <- function() {
  mvtnorm(X,Z1,Z2) %>% 
  mutate(Y~X+Z1+Z2) %>% 
  for (max_iterations in 1:100) {
    ampute() %>% 
    impute() %T% 
    convergence_diagnostics %>% 
    lm(Y~X+Z1+Z2) %>% 
    pool %>% 
    simulation_diagnostics %>% 
    c(., convergence_diagnostics)
  }
}

replicate(simulation, n = 1000)

Simulation Results

Results

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Results

simulation diagnostics versus convergence diagnostics

Inspect the autocorrelation dip

Discussion

Discussion

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Impression of ShinyMICE


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Inspect \(\widehat{R}\)

Inspect autocorrelation

Equations \(\widehat{R}\)

[]

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References

Allison, Paul D. 2001. Missing Data. Sage publications.

Rubin, Donald B. 1987. Multiple Imputation for Nonresponse in Surveys. Wiley Series in Probability and Mathematical Statistics Applied Probability and Statistics. New York, NY: Wiley.

Van Buuren, Stef. 2018. Flexible Imputation of Missing Data. Chapman; Hall/CRC.